๐Ÿ“ W01 Formative Exercise

Visualising Insights from the EP Pillar (ASCOR Dataset)

Author

Dr Jon Cardoso-Silva

Published

21 January 2025

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Last updated: 21 January 2025, 19:30

โณ Duration

This exercise should take 1-2 hours, giving you ample time to explore, process, and reflect on the ASCOR dataset in preparation for Week 02. Make sure to pace yourself and reach out for support if needed.

Objective

Your goal is to create an alternative visualisation focusing on the EP Pillar in the ASCOR dataset. The visualisation should highlight key insights about the distribution of results (No, Partial, Yes) across countries for the relevant indicators (EP.1.b, EP.1.c, EP.2.a, etc.).

๐Ÿ’ก TIP: Start by replicating the plot below, which can be found on the ASCOR website. You can use facetting or sub-plots to achieve a similar result in Python.

Incentive

The most innovative, clean, insightful and self-explanatory visualisation will win a DSI tote bag! ๐Ÿ‘œ

What Weโ€™re Looking For

  1. Clarity and Engagement:
    • The plot title and subtitles must tell a clear and meaningful story.
    • Use visual encodings such as colours, sizes, or shapes to emphasise trends or disparities.
  2. Simplicity over Complexity:
    • Keep the design clean and easy to interpret.
    • Avoid over-complicating the visualisationโ€”focus on delivering or or two key insights clearly.
  3. Creativity:
    • Think outside the box while staying focused on clarity.
    • Explore how alternative visualisations might better communicate the dataโ€™s story.

Submission Guidelines

  1. Prepare the following files:

    • Visualisation: Export your plot as a .png or similar image file.
    • Jupyter Notebook: Include the source code you used to produce the visualisation.

  2. Upload: Post your files directly as a comment in this designated GitHub Issue (no need for branches or pull requests).

    Note: this is a public repository, donโ€™t add any information you donโ€™t want to share with the world. We will make it private later once Iโ€™ve added everyone as contributors to the repository.

  3. Naming: Use the following format for your notebook: DS205_W01_YourGitHubUserName.ipynb (e.g., DS205_W01_JaneDoe.ipynb).

  4. Deadline: Submit your work by the time of the W02 lecture, Monday, 27 January 2025, 09:59 am.

  5. Judging: The submissions will be judged by Jon, Alex, Barry, Sara and Kevin. The winner will be announced by Tuesday, 28 January 2025.